Introduction

Unnecessary violence employed by many police officers throughout the US is an issue that needs to be addressed and dealt with. Recently, police fatalities have been highlighted in the media for their often unjust natures. Black men especially seem to be more targeted than the rest of the population. Let’s take a look at police fatalities data that has been gathered by the Washington Post starting from 2015 to now to help us understand what demographic is at risk. We will take a look at the top 25 most fatal cities and break down the fatalities by race. We will also compare the race breakdown of the fatalities to the race breakdown of the population of the cities.

Package Installation

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## v readr   2.1.1     v forcats 0.5.1

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## Rows: 7246 Columns: 17

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (10): name, date, manner_of_death, armed, gender, race, city, state, thr...
## dbl  (4): id, age, longitude, latitude
## lgl  (3): signs_of_mental_illness, body_camera, is_geocoding_exact

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## -- Column specification --------------------------------------------------------
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Gender Breakdown

Here we can see the difference in the number of female versus male fatalities. This helps us understand the demographic that is in danger of being killed by a police officer.

## # A tibble: 3 x 2
##   gender count
##   <chr>  <int>
## 1 F        327
## 2 M       6913
## 3 <NA>       6

Police Fatalities

Some text about this table and how interesting the results are!

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
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Race Breakdown of Police Fatalities in the top 25 Most Fatal Cities

Using these graphs we can see the top 25 cities with the most police fatalities. The first graph shows us the race breakdown in numbers and the second one shows us the race breakdown in percentage.

## # A tibble: 7,246 x 18
## # Groups:   date [2,471]
##       id name    date       manner_of_death armed   age gender race  city  state
##    <dbl> <chr>   <date>     <chr>           <chr> <dbl> <chr>  <chr> <chr> <chr>
##  1     3 Tim El~ 2015-01-02 shot            gun      53 M      Asian Shel~ WA   
##  2     4 Lewis ~ 2015-01-02 shot            gun      47 M      White Aloha OR   
##  3     5 John P~ 2015-01-03 shot and Taser~ unar~    23 M      Hisp~ Wich~ KS   
##  4     8 Matthe~ 2015-01-04 shot            toy ~    32 M      White San ~ CA   
##  5     9 Michae~ 2015-01-04 shot            nail~    39 M      Hisp~ Evans CO   
##  6    11 Kennet~ 2015-01-04 shot            gun      18 M      White Guth~ OK   
##  7    13 Kennet~ 2015-01-05 shot            gun      22 M      Hisp~ Chan~ AZ   
##  8    15 Brock ~ 2015-01-06 shot            gun      35 M      White Assa~ KS   
##  9    16 Autumn~ 2015-01-06 shot            unar~    34 F      White Burl~ IA   
## 10    17 Leslie~ 2015-01-06 shot            toy ~    47 M      Black Knox~ PA   
## # ... with 7,236 more rows, and 8 more variables:
## #   signs_of_mental_illness <lgl>, threat_level <chr>, flee <chr>,
## #   body_camera <lgl>, longitude <dbl>, latitude <dbl>,
## #   is_geocoding_exact <lgl>, count <int>